






"""simple docstring"""





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def 	lowerCamelCase__			(				_lowerCamelCase			:						Dict						,							_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						List[str]					) ->							Optional[Any]:
  return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]







def 	lowerCamelCase__			(				_lowerCamelCase			:						str						,							_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						Union[str, Any]						,							_lowerCamelCase			:						Optional[int]="attention"					) ->							str:
  lowerCamelCase_       	=		lowerCamelCase_       	=		np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]					)
  lowerCamelCase_       	=		k_tmp.reshape(k_tmp.shape[0]						,							k_tmp.shape[1] * k_tmp.shape[2]					)
  lowerCamelCase_       	=		np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]					)
  lowerCamelCase_       	=		o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,							o_tmp.shape[2]					)
  lowerCamelCase_       	=		np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]					)
  lowerCamelCase_       	=		q_tmp.reshape(q_tmp.shape[0]						,							q_tmp.shape[1] * q_tmp.shape[2]					)
  lowerCamelCase_       	=		np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]					)
  lowerCamelCase_       	=		v_tmp.reshape(v_tmp.shape[0]						,							v_tmp.shape[1] * v_tmp.shape[2]					)
  return k, o, q, v







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[int]						,							_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						str						,							_lowerCamelCase			:						Tuple=False					) ->							Optional[int]:
  if split_mlp_wi:
    lowerCamelCase_       	=		params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
    lowerCamelCase_       	=		params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
    lowerCamelCase_       	=		(wi_a, wi_a)
  else:
    lowerCamelCase_       	=		params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

  lowerCamelCase_       	=		params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
  return wi, wo







def 	lowerCamelCase__			(				_lowerCamelCase			:						List[Any]						,							_lowerCamelCase			:						int						,							_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						Optional[int]					) ->							int:
  return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]







def 	lowerCamelCase__			(				_lowerCamelCase			:						dict						,							*, _lowerCamelCase			:						int						,							_lowerCamelCase			:						bool						,							_lowerCamelCase			:						bool = False					) ->							str:
  lowerCamelCase_       	=		traverse_util.flatten_dict(variables['target']					)
  lowerCamelCase_       	=		{'/'.join(_lowerCamelCase					): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  lowerCamelCase_       	=		'encoder/encoder/mlp/wi_0/kernel' in old
  print('Split MLP:'						,							_lowerCamelCase					)

  lowerCamelCase_       	=		collections.OrderedDict()

  # Shared embeddings.
  lowerCamelCase_       	=		old['token_embedder/embedding']

  # Encoder.
  for i in range(_lowerCamelCase					):
    # Block i, layer 0 (Self Attention).
    lowerCamelCase_       	=		tax_layer_norm_lookup(_lowerCamelCase						,							_lowerCamelCase						,							'encoder'						,							'pre_attention_layer_norm'					)
    lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		tax_attention_lookup(_lowerCamelCase						,							_lowerCamelCase						,							'encoder'						,							'attention'					)
    lowerCamelCase_       	=		layer_norm
    lowerCamelCase_       	=		k.T
    lowerCamelCase_       	=		o.T
    lowerCamelCase_       	=		q.T
    lowerCamelCase_       	=		v.T

    # Block i, layer 1 (MLP).
    lowerCamelCase_       	=		tax_layer_norm_lookup(_lowerCamelCase						,							_lowerCamelCase						,							'encoder'						,							'pre_mlp_layer_norm'					)
    lowerCamelCase_	,      lowerCamelCase_       	=		tax_mlp_lookup(_lowerCamelCase						,							_lowerCamelCase						,							'encoder'						,							_lowerCamelCase					)
    lowerCamelCase_       	=		layer_norm
    if split_mlp_wi:
      lowerCamelCase_       	=		wi[0].T
      lowerCamelCase_       	=		wi[1].T
    else:
      lowerCamelCase_       	=		wi.T
    lowerCamelCase_       	=		wo.T
    if scalable_attention:
      # convert the rel_embedding of each layer
      lowerCamelCase_       	=		tax_relpos_bias_lookup(
          _lowerCamelCase						,							_lowerCamelCase						,							'encoder'					).T

  lowerCamelCase_       	=		old['encoder/encoder_norm/scale']

  if not scalable_attention:
    lowerCamelCase_       	=		tax_relpos_bias_lookup(
        _lowerCamelCase						,							0						,							'encoder'					).T
    lowerCamelCase_       	=		tax_relpos_bias_lookup(
        _lowerCamelCase						,							0						,							'decoder'					).T

  if not is_encoder_only:
    # Decoder.
    for i in range(_lowerCamelCase					):
      # Block i, layer 0 (Self Attention).
      lowerCamelCase_       	=		tax_layer_norm_lookup(_lowerCamelCase						,							_lowerCamelCase						,							'decoder'						,							'pre_self_attention_layer_norm'					)
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		tax_attention_lookup(_lowerCamelCase						,							_lowerCamelCase						,							'decoder'						,							'self_attention'					)
      lowerCamelCase_       	=		layer_norm
      lowerCamelCase_       	=		k.T
      lowerCamelCase_       	=		o.T
      lowerCamelCase_       	=		q.T
      lowerCamelCase_       	=		v.T

      # Block i, layer 1 (Cross Attention).
      lowerCamelCase_       	=		tax_layer_norm_lookup(_lowerCamelCase						,							_lowerCamelCase						,							'decoder'						,							'pre_cross_attention_layer_norm'					)
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		tax_attention_lookup(_lowerCamelCase						,							_lowerCamelCase						,							'decoder'						,							'encoder_decoder_attention'					)
      lowerCamelCase_       	=		layer_norm
      lowerCamelCase_       	=		k.T
      lowerCamelCase_       	=		o.T
      lowerCamelCase_       	=		q.T
      lowerCamelCase_       	=		v.T

      # Block i, layer 2 (MLP).
      lowerCamelCase_       	=		tax_layer_norm_lookup(_lowerCamelCase						,							_lowerCamelCase						,							'decoder'						,							'pre_mlp_layer_norm'					)
      lowerCamelCase_	,      lowerCamelCase_       	=		tax_mlp_lookup(_lowerCamelCase						,							_lowerCamelCase						,							'decoder'						,							_lowerCamelCase					)
      lowerCamelCase_       	=		layer_norm
      if split_mlp_wi:
        lowerCamelCase_       	=		wi[0].T
        lowerCamelCase_       	=		wi[1].T
      else:
        lowerCamelCase_       	=		wi.T
      lowerCamelCase_       	=		wo.T

      if scalable_attention:
        # convert the rel_embedding of each layer
        lowerCamelCase_       	=		tax_relpos_bias_lookup(_lowerCamelCase						,							_lowerCamelCase						,							'decoder'					).T

    lowerCamelCase_       	=		old['decoder/decoder_norm/scale']

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      lowerCamelCase_       	=		old['decoder/logits_dense/kernel'].T

  return new







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						bool					) ->							Tuple:
  lowerCamelCase_       	=		collections.OrderedDict([(k, torch.from_numpy(v.copy()					)) for (k, v) in converted_params.items()]					)

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    lowerCamelCase_       	=		state_dict['shared.weight']

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      lowerCamelCase_       	=		state_dict['shared.weight']

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print('Using shared word embeddings as lm_head.'					)
      lowerCamelCase_       	=		state_dict['shared.weight']

  return state_dict







def 	lowerCamelCase__			(				_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						int						,							_lowerCamelCase			:						Any						,							_lowerCamelCase			:						Union[str, Any]						,							_lowerCamelCase			:						Optional[Any]					) ->							Optional[Any]:
  lowerCamelCase_       	=		checkpoints.load_tax_checkpoint(_lowerCamelCase					)
  lowerCamelCase_       	=		convert_tax_to_pytorch(
      _lowerCamelCase						,							num_layers=config.num_layers						,							is_encoder_only=_lowerCamelCase						,							scalable_attention=_lowerCamelCase					)
  lowerCamelCase_       	=		make_state_dict(_lowerCamelCase						,							_lowerCamelCase					)
  model.load_state_dict(_lowerCamelCase						,							strict=_lowerCamelCase					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						Tuple						,							_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						Optional[Any]						,							_lowerCamelCase			:						bool = False						,							_lowerCamelCase			:						bool = False						,							) ->							Optional[Any]:
  lowerCamelCase_       	=		MTaConfig.from_json_file(_lowerCamelCase					)
  print(F'''Building PyTorch model from configuration: {config}'''					)
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    lowerCamelCase_       	=		UMTaEncoderModel(_lowerCamelCase					)
  else:
    lowerCamelCase_       	=		UMTaForConditionalGeneration(_lowerCamelCase					)

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''					)
  model.save_pretrained(_lowerCamelCase					)

  # Verify that we can load the checkpoint.
  model.from_pretrained(_lowerCamelCase					)
  print('Done'					)


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE   :							str          =		argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
  # Required parameters
  parser.add_argument(
      '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
  )
  parser.add_argument(
      '''--config_file''',
      default=None,
      type=str,
      required=True,
      help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
  )
  parser.add_argument(
      '''--scalable_attention''',
      action='''store_true''',
      help='''Whether the model uses scaled attention (umt5 model)''',
      default=False,
  )
  _SCREAMING_SNAKE_CASE   :							Tuple          =		parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path,
      args.config_file,
      args.pytorch_dump_path,
      args.is_encoder_only,
      args.scalable_attention,
  )












"""simple docstring"""





import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer






@require_tf
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Dict   )	->						str:
      self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		len(__SCREAMING_SNAKE_CASE   )   )
      for a, b in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        self.assertAlmostEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		delta=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						int:
      lowerCamelCase_       	=		GradientAccumulator()
      accumulator([tf.constant([1.0, 2.0]   )]   )
      accumulator([tf.constant([-2.0, 1.0]   )]   )
      accumulator([tf.constant([-1.0, 2.0]   )]   )
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        accumulator([tf.constant([1.0, 1.0]   ), tf.constant([2.0, 2.0]   )]   )
      self.assertEqual(accumulator.step			,		3   )
      self.assertEqual(len(accumulator.gradients   )			,		1   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[-2.0, 5.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[0.0, 0.0]			,		tol=1e-2   )






    def   UpperCamelCase			(			self	:				str   )	->						Any:
      lowerCamelCase_       	=		None
      ops.enable_eager_execution_internal()
      lowerCamelCase_       	=		tf.config.list_physical_devices('CPU'   )
      if len(__SCREAMING_SNAKE_CASE   ) == 1:
        tf.config.set_logical_device_configuration(
            physical_devices[0]			,		[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]   )
      lowerCamelCase_       	=		tf.config.list_logical_devices(device_type='CPU'   )
      lowerCamelCase_       	=		tf.distribute.MirroredStrategy(devices=devices[:2]   )

      with strategy.scope():
        lowerCamelCase_       	=		GradientAccumulator()
        lowerCamelCase_       	=		tf.Variable([4.0, 3.0]   )
        lowerCamelCase_	,      lowerCamelCase_       	=		create_optimizer(5e-5			,		10			,		5   )
        lowerCamelCase_       	=		tf.Variable([0.0, 0.0]			,		trainable=__SCREAMING_SNAKE_CASE   )

      def accumulate_on_replica(__SCREAMING_SNAKE_CASE	:				Union[str, Any]   ):
        accumulator([gradient]   )

      def apply_on_replica():
        optimizer.apply_gradients(list(zip(accumulator.gradients			,		[variable]   )   )   )

      @tf.function
      def accumulate(__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any   ):
        with strategy.scope():
          lowerCamelCase_       	=		strategy.experimental_local_results(__SCREAMING_SNAKE_CASE   )
          local_variables[0].assign(__SCREAMING_SNAKE_CASE   )
          local_variables[1].assign(__SCREAMING_SNAKE_CASE   )
          strategy.run(__SCREAMING_SNAKE_CASE			,		args=(gradient_placeholder,)   )

      @tf.function
      def apply_grad():
        with strategy.scope():
          strategy.run(__SCREAMING_SNAKE_CASE   )

      def _check_local_values(__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				List[str]   ):
        lowerCamelCase_       	=		strategy.experimental_local_results(accumulator._gradients[0]   )
        self.assertListAlmostEqual(values[0].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )
        self.assertListAlmostEqual(values[1].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )

      accumulate([1.0, 2.0]			,		[-1.0, 1.0]   )
      accumulate([3.0, -1.0]			,		[-1.0, -1.0]   )
      accumulate([-2.0, 2.0]			,		[3.0, -2.0]   )
      self.assertEqual(accumulator.step			,		3   )
      _check_local_values([2.0, 3.0]			,		[1.0, -2.0]   )
      apply_grad()
      self.assertListAlmostEqual(variable.value()			,		[4.0, 3.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      _check_local_values([0.0, 0.0]			,		[0.0, 0.0]   )




